# coding=utf-8
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Testing suite for the Paddle Chinese-CLIP model. """
import copy
import inspect
import tempfile
import unittest

import numpy as np
import paddle
import paddle.nn as nn
import requests
from PIL import Image

from paddlenlp.transformers import (
    ChineseCLIPConfig,
    ChineseCLIPModel,
    ChineseCLIPProcessor,
    ChineseCLIPTextConfig,
    ChineseCLIPTextModel,
    ChineseCLIPTextModelWithProjection,
    ChineseCLIPVisionConfig,
    ChineseCLIPVisionModel,
    ChineseCLIPVisionModelWithProjection,
)
from paddlenlp.transformers.chineseclip.modeling import (
    CHINESE_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
    ChineseCLIPVisionTransformer,
)

from ...testing_utils import slow
from ..test_configuration_common import ConfigTester
from ..test_modeling_common import (
    ModelTesterMixin,
    floats_tensor,
    ids_tensor,
    random_attention_mask,
)


def _config_zero_init(config):
    configs_no_init = copy.deepcopy(config)
    for key in configs_no_init.__dict__.keys():
        if "_range" in key or "_std" in key or "initializer_factor" in key or "layer_scale" in key:
            setattr(configs_no_init, key, 1e-10)
    return configs_no_init


class ChineseCLIPTextModelTester:
    def __init__(
        self,
        parent,
        batch_size=12,
        seq_length=7,
        is_training=True,
        use_input_mask=True,
        use_labels=True,
        vocab_size=99,
        hidden_size=32,
        type_vocab_size=2,
        projection_dim=32,
        num_hidden_layers=5,
        num_attention_heads=4,
        intermediate_size=37,
        dropout=0.1,
        attention_dropout=0.1,
        max_position_embeddings=512,
        initializer_range=0.02,
        scope=None,
    ):
        self.parent = parent
        self.batch_size = batch_size
        self.seq_length = seq_length
        self.is_training = is_training
        self.use_input_mask = use_input_mask
        self.use_labels = use_labels
        self.vocab_size = vocab_size
        self.hidden_size = hidden_size
        self.projection_dim = projection_dim
        self.num_hidden_layers = num_hidden_layers
        self.num_attention_heads = num_attention_heads
        self.intermediate_size = intermediate_size
        self.dropout = dropout
        self.attention_dropout = attention_dropout
        self.max_position_embeddings = max_position_embeddings
        self.initializer_range = initializer_range
        self.type_vocab_size = type_vocab_size
        self.scope = scope

    def prepare_config_and_inputs(self):
        input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)

        input_mask = None
        if self.use_input_mask:
            input_mask = random_attention_mask([self.batch_size, self.seq_length])

        if input_mask is not None:
            batch_size, seq_length = input_mask.shape
            rnd_start_indices = np.random.randint(1, seq_length - 1, size=(batch_size,))
            for batch_idx, start_index in enumerate(rnd_start_indices):
                input_mask[batch_idx, :start_index] = 1
                input_mask[batch_idx, start_index:] = 0

        token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)

        config = self.get_config()

        return config, input_ids, token_type_ids, input_mask

    def get_config(self):
        return ChineseCLIPTextConfig(
            vocab_size=self.vocab_size,
            hidden_size=self.hidden_size,
            projection_dim=self.projection_dim,
            num_hidden_layers=self.num_hidden_layers,
            num_attention_heads=self.num_attention_heads,
            intermediate_size=self.intermediate_size,
            dropout=self.dropout,
            attention_dropout=self.attention_dropout,
            max_position_embeddings=self.max_position_embeddings,
            initializer_range=self.initializer_range,
        )

    def create_and_check_model(self, config, input_ids, token_type_ids, input_mask):
        model = ChineseCLIPTextModel(config=config)
        model.eval()
        with paddle.no_grad():
            result = model(input_ids, token_type_ids=token_type_ids, attention_mask=input_mask)
        self.parent.assertEqual(result.last_hidden_state.shape, [self.batch_size, self.seq_length, self.hidden_size])
        self.parent.assertEqual(result.pooler_output.shape, [self.batch_size, self.hidden_size])

    def create_and_check_model_with_projection(self, config, input_ids, token_type_ids, input_mask):
        model = ChineseCLIPTextModelWithProjection(config=config)
        model.eval()
        with paddle.no_grad():
            result = model(input_ids, token_type_ids=token_type_ids, attention_mask=input_mask)
        self.parent.assertEqual(result.last_hidden_state.shape, [self.batch_size, self.seq_length, self.hidden_size])
        self.parent.assertEqual(result.text_embeds.shape, [self.batch_size, self.projection_dim])

    def prepare_config_and_inputs_for_common(self):
        config_and_inputs = self.prepare_config_and_inputs()
        config, input_ids, token_type_ids, input_mask = config_and_inputs
        inputs_dict = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
        return config, inputs_dict


class ChineseCLIPTextModelTest(ModelTesterMixin, unittest.TestCase):

    all_model_classes = (ChineseCLIPTextModel, ChineseCLIPTextModelWithProjection)
    use_test_model_name_list = False

    def setUp(self):
        self.model_tester = ChineseCLIPTextModelTester(self)
        self.config_tester = ConfigTester(self, config_class=ChineseCLIPTextConfig, hidden_size=37)

    def test_config(self):
        self.config_tester.run_common_tests()

    def test_model(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*config_and_inputs)

    def test_model_with_projection(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model_with_projection(*config_and_inputs)

    def test_training(self):
        pass

    def test_training_gradient_checkpointing(self):
        pass

    @unittest.skip(reason="ChineseCLIPTextModel does not use inputs_embeds")
    def test_inputs_embeds(self):
        pass

    @unittest.skip(reason="ChineseCLIPTextModel has no base class and is not available in MODEL_MAPPING")
    def test_save_load_fast_init_from_base(self):
        pass

    @unittest.skip(reason="ChineseCLIPTextModel has no base class and is not available in MODEL_MAPPING")
    def test_save_load_fast_init_to_base(self):
        pass

    @slow
    def test_model_from_pretrained(self):
        for model_name in CHINESE_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            model = ChineseCLIPTextModel.from_pretrained(model_name)
            self.assertIsNotNone(model)

    @slow
    def test_model_with_projection_from_pretrained(self):
        for model_name in CHINESE_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            model = ChineseCLIPTextModelWithProjection.from_pretrained(model_name)
            self.assertIsNotNone(model)
            self.assertTrue(hasattr(model, "text_projection"))


class ChineseCLIPVisionModelTester:
    def __init__(
        self,
        parent,
        batch_size=12,
        image_size=30,
        patch_size=2,
        num_channels=3,
        is_training=True,
        hidden_size=32,
        projection_dim=32,
        num_hidden_layers=5,
        num_attention_heads=4,
        intermediate_size=37,
        dropout=0.1,
        attention_dropout=0.1,
        initializer_range=0.02,
        scope=None,
    ):
        self.parent = parent
        self.batch_size = batch_size
        self.image_size = image_size
        self.patch_size = patch_size
        self.num_channels = num_channels
        self.is_training = is_training
        self.hidden_size = hidden_size
        self.projection_dim = projection_dim
        self.num_hidden_layers = num_hidden_layers
        self.num_attention_heads = num_attention_heads
        self.intermediate_size = intermediate_size
        self.dropout = dropout
        self.attention_dropout = attention_dropout
        self.initializer_range = initializer_range
        self.scope = scope

        # in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
        num_patches = (image_size // patch_size) ** 2
        self.seq_length = num_patches + 1

    def prepare_config_and_inputs(self):
        pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
        config = self.get_config()

        return config, pixel_values

    def get_config(self):
        return ChineseCLIPVisionConfig(
            image_size=self.image_size,
            patch_size=self.patch_size,
            num_channels=self.num_channels,
            hidden_size=self.hidden_size,
            projection_dim=self.projection_dim,
            num_hidden_layers=self.num_hidden_layers,
            num_attention_heads=self.num_attention_heads,
            intermediate_size=self.intermediate_size,
            dropout=self.dropout,
            attention_dropout=self.attention_dropout,
            initializer_range=self.initializer_range,
        )

    def create_and_check_model(self, config, pixel_values):
        model = ChineseCLIPVisionModel(config=config)
        model.eval()
        with paddle.no_grad():
            result = model(pixel_values)
        # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
        image_size = (self.image_size, self.image_size)
        patch_size = (self.patch_size, self.patch_size)
        num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
        self.parent.assertEqual(result.last_hidden_state.shape, [self.batch_size, num_patches + 1, self.hidden_size])
        self.parent.assertEqual(result.pooler_output.shape, [self.batch_size, self.hidden_size])

    def create_and_check_model_with_projection(self, config, pixel_values):
        model = ChineseCLIPVisionModelWithProjection(config=config)
        model.eval()
        with paddle.no_grad():
            result = model(pixel_values)
        # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
        image_size = (self.image_size, self.image_size)
        patch_size = (self.patch_size, self.patch_size)
        num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
        self.parent.assertEqual(result.last_hidden_state.shape, [self.batch_size, num_patches + 1, self.hidden_size])
        self.parent.assertEqual(result.image_embeds.shape, [self.batch_size, self.projection_dim])

    def prepare_config_and_inputs_for_common(self):
        config_and_inputs = self.prepare_config_and_inputs()
        config, pixel_values = config_and_inputs
        inputs_dict = {"pixel_values": pixel_values}
        return config, inputs_dict


class ChineseCLIPVisionModelTest(ModelTesterMixin, unittest.TestCase):
    """
    Here we also overwrite some of the tests of test_modeling_common.py, as CHINESE_CLIP does not use input_ids, inputs_embeds,
    attention_mask and seq_length.
    """

    all_model_classes = (ChineseCLIPVisionModel, ChineseCLIPVisionModelWithProjection)
    test_resize_embeddings = False
    use_test_model_name_list = False

    def setUp(self):
        self.model_tester = ChineseCLIPVisionModelTester(self)
        self.config_tester = ConfigTester(
            self, config_class=ChineseCLIPVisionConfig, has_text_modality=False, hidden_size=37
        )

    def test_config(self):
        self.config_tester.run_common_tests()

    @unittest.skip(reason="CHINESE_CLIP does not use inputs_embeds")
    def test_inputs_embeds(self):
        pass

    def test_model_common_attributes(self):
        config, _ = self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
            model = model_class(config)
            self.assertIsInstance(model.get_input_embeddings(), (nn.Layer))
            x = model.get_output_embeddings()
            self.assertTrue(x is None or isinstance(x, nn.Linear))

    def test_forward_signature(self):
        config, _ = self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
            model = model_class(config)
            signature = inspect.signature(model.forward)
            # signature.parameters is an OrderedDict => so arg_names order is deterministic
            arg_names = [*signature.parameters.keys()]

            expected_arg_names = ["pixel_values"]
            self.assertListEqual(arg_names[:1], expected_arg_names)

    def test_model(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*config_and_inputs)

    def test_model_with_projection(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model_with_projection(*config_and_inputs)

    def test_training(self):
        pass

    def test_training_gradient_checkpointing(self):
        pass

    @unittest.skip(reason="ChineseCLIPVisionModel has no base class and is not available in MODEL_MAPPING")
    def test_save_load_fast_init_from_base(self):
        pass

    @unittest.skip(reason="ChineseCLIPVisionModel has no base class and is not available in MODEL_MAPPING")
    def test_save_load_fast_init_to_base(self):
        pass

    @slow
    def test_model_from_pretrained(self):
        for model_name in CHINESE_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            model = ChineseCLIPVisionModel.from_pretrained(model_name)
            self.assertIsNotNone(model)

    @slow
    def test_model_with_projection_from_pretrained(self):
        for model_name in CHINESE_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            model = ChineseCLIPVisionModelWithProjection.from_pretrained(model_name)
            self.assertIsNotNone(model)
            if isinstance(model.vision_model, ChineseCLIPVisionTransformer):
                self.assertTrue(hasattr(model, "vision_projection"))


class ChineseCLIPModelTester:
    def __init__(self, parent, text_kwargs=None, vision_kwargs=None, is_training=True):

        if text_kwargs is None:
            text_kwargs = {}
        if vision_kwargs is None:
            vision_kwargs = {}

        self.parent = parent
        self.text_model_tester = ChineseCLIPTextModelTester(parent, **text_kwargs)
        self.vision_model_tester = ChineseCLIPVisionModelTester(parent, **vision_kwargs)
        self.is_training = is_training

    def prepare_config_and_inputs(self):
        text_config, input_ids, token_type_ids, attention_mask = self.text_model_tester.prepare_config_and_inputs()
        vision_config, pixel_values = self.vision_model_tester.prepare_config_and_inputs()

        config = self.get_config()

        return config, input_ids, token_type_ids, attention_mask, pixel_values

    def get_config(self):
        return ChineseCLIPConfig.from_text_vision_configs(
            self.text_model_tester.get_config(), self.vision_model_tester.get_config(), projection_dim=64
        )

    def create_and_check_model(self, config, input_ids, token_type_ids, attention_mask, pixel_values):
        model = ChineseCLIPModel(config)
        model.eval()
        with paddle.no_grad():
            result = model(input_ids, pixel_values, attention_mask=attention_mask)
        self.parent.assertEqual(
            result.logits_per_image.shape, [self.vision_model_tester.batch_size, self.text_model_tester.batch_size]
        )
        self.parent.assertEqual(
            result.logits_per_text.shape, [self.text_model_tester.batch_size, self.vision_model_tester.batch_size]
        )

    def prepare_config_and_inputs_for_common(self):
        config_and_inputs = self.prepare_config_and_inputs()
        config, input_ids, token_type_ids, attention_mask, pixel_values = config_and_inputs
        inputs_dict = {
            "input_ids": input_ids,
            "token_type_ids": token_type_ids,
            "attention_mask": attention_mask,
            "pixel_values": pixel_values,
            "return_loss": True,
        }
        return config, inputs_dict


class ChineseCLIPModelTest(ModelTesterMixin, unittest.TestCase):
    all_model_classes = (ChineseCLIPModel,)
    test_resize_embeddings = False
    test_attention_outputs = False
    use_test_model_name_list = False

    def setUp(self):
        text_kwargs = {"batch_size": 12}
        vision_kwargs = {"batch_size": 12}
        self.model_tester = ChineseCLIPModelTester(self, text_kwargs, vision_kwargs)

    def test_model(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*config_and_inputs)

    @unittest.skip(reason="Hidden_states is tested in individual model tests")
    def test_hidden_states_output(self):
        pass

    @unittest.skip(reason="Inputs_embeds is tested in individual model tests")
    def test_inputs_embeds(self):
        pass

    @unittest.skip(reason="Retain_grad is tested in individual model tests")
    def test_retain_grad_hidden_states_attentions(self):
        pass

    @unittest.skip(reason="ChineseCLIPModel does not have input/output embeddings")
    def test_model_common_attributes(self):
        pass

    # override as the `logit_scale` parameter initialization is different for CHINESE_CLIP
    def test_initialization(self):
        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()

        configs_no_init = _config_zero_init(config)
        for sub_config_key in ("vision_config", "text_config"):
            sub_config = getattr(configs_no_init, sub_config_key, {})
            setattr(configs_no_init, sub_config_key, _config_zero_init(sub_config))
        for model_class in self.all_model_classes:
            model = model_class(config=configs_no_init)
            for name, param in model.named_parameters():
                if not param.stop_gradient:
                    # check if `logit_scale` is initialized as per the original implementation
                    if name == "logit_scale":
                        self.assertAlmostEqual(
                            param.item(),
                            np.log(1 / 0.07),
                            delta=1e-3,
                            msg=f"Parameter {name} of model {model_class} seems not properly initialized",
                        )
                    else:
                        self.assertIn(
                            ((param.mean() * 1e9).round() / 1e9).item(),
                            [0.0, 1.0],
                            msg=f"Parameter {name} of model {model_class} seems not properly initialized",
                        )

    def test_load_vision_text_config(self):
        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()

        # Save CLIPConfig and check if we can load CLIPVisionConfig from it
        with tempfile.TemporaryDirectory() as tmp_dir_name:
            config.save_pretrained(tmp_dir_name)
            vision_config = ChineseCLIPVisionConfig.from_pretrained(tmp_dir_name)
            self.assertDictEqual(config.vision_config.to_dict(), vision_config.to_dict())

        # Save CLIPConfig and check if we can load CLIPTextConfig from it
        with tempfile.TemporaryDirectory() as tmp_dir_name:
            config.save_pretrained(tmp_dir_name)
            text_config = ChineseCLIPTextConfig.from_pretrained(tmp_dir_name)
            self.assertDictEqual(config.text_config.to_dict(), text_config.to_dict())

    @slow
    def test_model_from_pretrained(self):
        for model_name in CHINESE_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            model = ChineseCLIPModel.from_pretrained(model_name)
            self.assertIsNotNone(model)


# We will verify our results on an image of Pikachu
def prepare_img():
    url = "https://clip-cn-beijing.oss-cn-beijing.aliyuncs.com/pokemon.jpeg"
    im = Image.open(requests.get(url, stream=True).raw)
    return im


class ChineseCLIPModelIntegrationTest(unittest.TestCase):
    @slow
    def test_inference(self):
        model_name = "OFA-Sys/chinese-clip-vit-base-patch16"
        model = ChineseCLIPModel.from_pretrained(model_name)
        model.eval()
        processor = ChineseCLIPProcessor.from_pretrained(model_name)

        image = prepare_img()
        inputs = processor(text=["杰尼龟", "妙蛙种子", "小火龙", "皮卡丘"], images=image, padding=True, return_tensors="pd")

        # forward pass
        with paddle.no_grad():
            outputs = model(**inputs)

        # verify the logits
        self.assertEqual(
            outputs.logits_per_image.shape,
            [inputs.pixel_values.shape[0], inputs.input_ids.shape[0]],
        )
        self.assertEqual(
            outputs.logits_per_text.shape,
            [inputs.input_ids.shape[0], inputs.pixel_values.shape[0]],
        )

        probs = paddle.nn.functional.softmax(outputs.logits_per_image, axis=1)
        expected_probs = paddle.to_tensor([[1.2686e-03, 5.4499e-02, 6.7968e-04, 9.4355e-01]])

        self.assertTrue(paddle.allclose(probs, expected_probs, atol=5e-3))
